Pay attention to the basal ganglia: a volumetric study in early dementia with Lewy bodies

Cytokine expression patterns predict suppression of vulnerable neural circuits in a mouse model of Alzheimer's disease

Alzheimer's disease is a neurodegenerative disorder characterized by progressive amyloid plaque accumulation, tau tangle formation, neuroimmune dysregulation, synapse an neuron loss, and changes in neural circuit activation that lead to cognitive decline and dementia. Early molecular and cellular disease-instigating events occur 20 or more years prior to presentation of symptoms, making them difficult to study, and for many years amyloid-β, the aggregating peptide seeding amyloid plaques, was thought to be the toxic factor responsible for cognitive deficit. However, strategies targeting amyloid-β aggregation and deposition have largely failed to produce safe and effective therapies, and amyloid plaque levels poorly correlate with cognitive outcomes. However, a role still exists for amyloid-β in the variation in an individual's immune response to early, soluble forms of aggregates, and the downstream consequences of this immune response for aberrant cellular behaviors and creation of a detrimental tissue environment that harms neuron health and causes changes in neural circuit activation. Here, we perform functional magnetic resonance imaging of awake, unanesthetized Alzheimer's disease mice to map changes in functional connectivity over the course of disease progression, in comparison to wild-type littermates. In these same individual animals, we spatiotemporally profile the immune milieu by measuring cytokines, chemokines, and growth factors across various brain regions and over the course of disease progression from pre-pathology through established cognitive deficit. We identify specific signatures of immune activation predicting hyperactivity followed by suppression of intra- and then inter-regional functional connectivity in multiple disease-relevant brain regions, following the pattern of spread of amyloid pathology.

Cortical and subcortical morphometric changes and their relation to cognitive impairment in isolated REM sleep behavior disorder

OBJECTIVE

To date, very few studies have focused on structural changes and their association with cognitive performance in isolated REM sleep behaviour disorder (iRBD). Moreover, the results of these studies are inconclusive. This study aims to evaluate differences in the associations between brain morphology and cognitive tests in iRBD and healthy controls.

METHODS

Sixty-three patients with iRBD and thirty-six controls underwent MRI with a 3 T scanner. The cognitive performance was assessed by a comprehensive neuropsychological battery. Based on performance, the iRBD group was divided into two subgroups with (iRBD-MCI) and without mild cognitive impairment (iRBD-NC). The high-resolution T1-weighted images were analysed using an automated atlas segmentation tool, voxel-based (VBM) and deformation-based (DBM) morphometry to identify between-group differences and correlations with cognitive performance.

RESULTS

VBM, DBM and the comparison of ROI volumes yielded no significant differences between iRBD and controls. In the iRBD group, significant correlations in VBM were found between several cortical and subcortical structures primarily located in the temporal, parietal, occipital lobe, cerebellum, and basal ganglia and three cognitive tests assessing psychomotor speed and one memory test. Between-group analysis of cognition revealed a significant difference between iRBD-MCI and iRBD-NC in tests including a processing speed component.

CONCLUSIONS

iRBD shows deficits in several cognitive tests that correlate with morphological changes, the most prominent of which is in psychomotor speed and visual attention as measured by the TMT-A and associated with the volume of striatum, insula, cerebellum, temporal lobe, pallidum and amygdala.

Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification using Deep Multimodal Learning

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis for effective treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI have emerged as valuable tools for investigating AD-related brain alterations. However, the potential benefits of integrating these modalities using deep learning techniques remain unexplored. In this study, we propose a novel framework that fuses composite images of multiple rs-fMRI networks (called voxelwise intensity projection) and gray matter segmentation images through a deep learning approach for improved AD classification. We demonstrate the superiority of fMRI networks over commonly used metrics such as amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in capturing spatial maps critical for AD classification. We use a multi-channel convolutional neural network incorporating the AlexNet dropout architecture to effectively model spatial and temporal dependencies in the integrated data. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset of AD patients and cognitively normal (CN) validate the efficacy of our approach, showcasing improved classification performance of 94.12% test accuracy and an area under the curve (AUC) score of 97.79 compared to existing methods. Our results show that the fusion results generally outperformed the unimodal results. The saliency visualizations also show significant differences in the hippocampus, amygdala, putamen, caudate nucleus, and regions of basal ganglia which are in line with the previous neurobiological literature. Our research offers a novel method to enhance our grasp of AD pathology. By integrating data from various functional networks with structural MRI insights, we significantly improve diagnostic accuracy. This accuracy is further boosted by the effective visualization of this combined information. This lays the groundwork for further studies focused on providing a more accurate and personalized approach to AD diagnosis. The proposed framework and insights gained from fMRI networks provide a promising avenue for future research in deep multimodal fusion and neuroimaging analysis.

Mild cognitive impairment in dementia with Lewy bodies: an update and outlook

Dementia with Lewy bodies (DLB), the second most common degenerative neurocognitive disorder after Alzheimer disease (AD), is frequently preceded by a period of mild cognitive impairment (MCI), in which cognitive decline is associated with impairment of executive functions/attention, visuospatial deficits, or other cognitive domains and a variety of noncognitive and neuropsychiatric symptoms, many of which are similar but less severe than in prodromal AD. While 36-38% remain in the MCI state, at least the same will convert to dementia. Biomarkers are slowing of the EEG rhythms, atrophy of hippocampus and nucleus basalis of Meynert, temporoparietal hypoperfusion, signs of degeneration of the nigrostriatal dopaminergic, cholinergic and other neurotransmitter systems, and inflammation. Functional neuroimaging studies revealed disturbed connectivity of frontal and limbic networks associated with attention and cognitive controls, dopaminergic and cholinergic circuits manifested prior to overt brain atrophy. Sparse neuropathological data showed varying Lewy body and AD-related stages associated with atrophy of entorhinal, hippocampal, and mediotemporal cortices. Putative pathomechanisms of MCI are degeneration of limbic, dopaminergic, and cholinergic systems with Lewy pathology affecting specific neuroanatomical pathways associated with progressing AD-related lesions, but many pathobiological mechanisms involved in the development of MCI in LBD remain to be elucidated as a basis for early diagnosis and future adequate treatment modalities to prevent progression of this debilitating disorder.

Exome-wide analysis reveals role of LRP1 and additional novel loci in cognition

Cognitive functioning is heritable, with metabolic risk factors known to accelerate age-associated cognitive decline. Identifying genetic underpinnings of cognition is thus crucial. Here, we undertake single-variant and gene-based association analyses upon 6 neurocognitive phenotypes across 6 cognition domains in whole-exome sequencing data from 157,160 individuals of the UK Biobank cohort to expound the genetic architecture of human cognition. We report 20 independent loci associated with 5 cognitive domains while controlling for APOE isoform-carrier status and metabolic risk factors; 18 of which were not previously reported, and implicated genes relating to oxidative stress, synaptic plasticity and connectivity, and neuroinflammation. A subset of significant hits for cognition indicates mediating effects via metabolic traits. Some of these variants also exhibit pleiotropic effects on metabolic traits. We further identify previously unknown interactions of APOE variants with LRP1 (rs34949484 and others, suggestively significant), AMIGO1 (rs146766120; pAla25Thr, significant), and ITPR3 (rs111522866, significant), controlling for lipid and glycemic risks. Our gene-based analysis also suggests that APOC1 and LRP1 have plausible roles along shared pathways of amyloid beta (Aβ) and lipid and/or glucose metabolism in affecting complex processing speed and visual attention. In addition, we report pairwise suggestive interactions of variants harbored in these genes with APOE affecting visual attention. Our report based on this large-scale exome-wide study highlights the effects of neuronal genes, such as LRP1, AMIGO1, and other genomic loci, thus providing further evidence of the genetic underpinnings for cognition during aging.

Patterns of structure-function association in normal aging and in Alzheimer's disease: Screening for mild cognitive impairment and dementia with ML regression and classification models

Background

The combined analysis of imaging and functional modalities is supposed to improve diagnostics of neurodegenerative diseases with advanced data science techniques.

Objective

To get an insight into normal and accelerated brain aging by developing the machine learning models that predict individual performance in neuropsychological and cognitive tests from brain MRI. With these models we endeavor to look for patterns of brain structure-function association (SFA) indicative of mild cognitive impairment (MCI) and Alzheimer's dementia.

Materials and methods

We explored the age-related variability of cognitive and neuropsychological test scores in normal and accelerated aging and constructed regression models predicting functional performance in cognitive tests from brain radiomics data. The models were trained on the three study cohorts from ADNI dataset-cognitively normal individuals, patients with MCI or dementia-separately. We also looked for significant correlations between cortical parcellation volumes and test scores in the cohorts to investigate neuroanatomical differences in relation to cognitive status. Finally, we worked out an approach for the classification of the examinees according to the pattern of structure-function associations into the cohorts of the cognitively normal elderly and patients with MCI or dementia.

Results

In the healthy population, the global cognitive functioning slightly changes with age. It also remains stable across the disease course in the majority of cases. In healthy adults and patients with MCI or dementia, the trendlines of performance in digit symbol substitution test and trail making test converge at the approximated point of 100 years of age. According to the SFA pattern, we distinguish three cohorts: the cognitively normal elderly, patients with MCI, and dementia. The highest accuracy is achieved with the model trained to predict the mini-mental state examination score from voxel-based morphometry data. The application of the majority voting technique to models predicting results in cognitive tests improved the classification performance up to 91.95% true positive rate for healthy participants, 86.21%-for MCI and 80.18%-for dementia cases.

Conclusion

The machine learning model, when trained on the cases of this of that group, describes a disease-specific SFA pattern. The pattern serves as a "stamp" of the disease reflected by the model.

MRI data-driven clustering reveals different subtypes of Dementia with Lewy bodies

Dementia with Lewy bodies (DLB) is a neurodegenerative disorder with a wide heterogeneity of symptoms, which suggests the existence of different subtypes. We used data-driven analysis of magnetic resonance imaging (MRI) data to investigate DLB subtypes. We included 165 DLB from the Mayo Clinic and 3 centers from the European DLB consortium and performed a hierarchical cluster analysis to identify subtypes based on gray matter (GM) volumes. To characterize the subtypes, we used demographic and clinical data, as well as β-amyloid, tau, and cerebrovascular biomarkers at baseline, and cognitive decline over three years. We identified 3 subtypes: an older subtype with reduced cortical GM volumes, worse cognition, and faster cognitive decline (n = 49, 30%); a subtype with low GM volumes in fronto-occipital regions (n = 76, 46%); and a subtype of younger patients with the highest cortical GM volumes, proportionally lower GM volumes in basal ganglia and the highest frequency of cognitive fluctuations (n = 40, 24%). This study shows the existence of MRI subtypes in DLB, which may have implications for clinical workout, research, and therapeutic decisions.

Memory Outcome in Prodromal and Mild Dementia with Lewy Bodies and Alzheimer's Disease: A Longitudinal Study

BACKGROUND

Dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) are likely to induce memory impairments from the prodromal stage but, to our knowledge, no longitudinal study of these patients' memory profile has been conducted to date.

OBJECTIVE

The aim of our study was to describe the characteristics and the evolution of the long-term memory profile of patients with prodromal and mild DLB and AD.

METHODS

We collected verbal (RL/RI-16) and visual (DMS48) memory scores from 91 DLB patients, 28 AD patients, 15 patients with both conditions (DLB/AD), and 18 healthy control subjects at their inclusion visit and at 12, 24, and 48 months.

RESULTS

On the RL/RI-16, DLB patients performed better than AD patients in terms of total recall (p < 0.001), delayed total recall (p < 0.001), recognition (p = 0.031), and loss of information over time (p = 0.023). On the DMS48, differences between these two groups were not significant (p > 0.05). Longitudinally, the memory performance of DLB patients was stable over 48 months, unlike that of AD patients.

CONCLUSION

Four indicators were relevant to distinguish between DLB and AD patients in terms of memory performance: DLB patients benefitted greatly from semantic cueing, their recognition and consolidation abilities were well-preserved, and both their verbal and visual memory performance remained remarkably stable over four years. However, no performance differences between DLB and AD patients were found regarding visual memory, either qualitatively (memory profile) or quantitatively (severity of impairment), indicating the lesser relevance of this test in distinguishing between these two diseases.

Consistent Abnormalities in Metabolic Patterns of Lewy Body Dementias

BACKGROUND

Whether dementia with Lewy bodies (DLB) and Parkinson's disease (PD) dementia (PDD) represent the same disease, distinct entities, or conditions within the same spectrum remains controversial.

OBJECTIVE

The objective of this study was to provide new insight into this debate by separately identifying disease-specific metabolic patterns and comparing them with each other and with previously established PD-related pattern (PDRP).

METHODS

Patients with DLB (n = 67), patients with PDD (n = 50), and healthy control subjects (HCs; n = 15) with brain ¹⁸ F-fluorodeoxyglucose positron emission tomography were enrolled as cohorts A and B for pattern identification and validation, respectively. Patients with PD (n = 30) were included for discrimination. Twenty-one participants had two scans. The principal component analysis was applied for pattern identification (DLB-related pattern [DLBRP], PDD-related pattern [PDDRP]). Similarities and differences among three patterns were assessed by pattern topography, pattern expression, clinical correlations cross-sectionally, and pattern expression changes longitudinally.

RESULTS

DLBRP and PDDRP shared highly similar topographies, with relative hypometabolism mainly in the middle temporal gyrus, middle occipital gyrus, lingual gyrus, precuneus, cuneus, angular gyrus, superior and inferior parietal gyrus, middle and inferior frontal gyrus, cingulate, and caudate, and relative hypermetabolism in the cerebellum, putamen, thalamus, precentral/postcentral gyrus, and paracentral lobule, which were more extensive than the PDRP. Patients with DLB and PDD could not be distinguished successfully by any pattern, but patients with PD were easily recognized, especially by DLBRP and PDDRP. The pattern expression of DLBRP and PDDRP showed similar efficacy in cross-sectional disease severity assessment and longitudinal progression monitoring.

CONCLUSIONS

The consistent abnormalities in metabolic patterns of DLB and PDD might underline the potential continuum across the clinical spectrum from PD to DLB. © 2022 International Parkinson and Movement Disorder Society.

Processing speed dysfunction is associated with functional corticostriatal circuit alterations in childhood epilepsy with centrotemporal spikes: a PET and fMRI study

Purpose

Epilepsy with centrotemporal spikes (ECTS) is the most common epilepsy syndrome in children and usually presents with cognitive dysfunctions. However, little is known about the processing speed dysfunction and the associated neuroimaging mechanism in ECTS. This study aims to investigate the brain functional abnormality of processing speed dysfunction in ECTS patients by using the ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI).

Methods

This prospective study recruited twenty-eight ECTS patients who underwent the ¹⁸F-FDG PET, rs-fMRI, and neuropsychological examinations. Twenty children with extracranial tumors were included as PET controls, and 20 healthy children were recruited as MRI controls. The PET image analysis investigated glucose metabolism by determining standardized uptake value ratio (SUVR). The MRI image analysis explored abnormal functional connectivity (FC) within the cortical–striatal circuit through network-based statistical (NBS) analysis. Correlation analysis was performed to explore the relationship between SUVR, FC, and processing speed index (PSI).

Results

Compared with healthy controls, ECTS patients showed normal intelligence quotient but significantly decreased PSI (P = 0.04). PET analysis showed significantly decreased SUVRs within bilateral caudate, putamen, pallidum, left NAc, right rostral middle frontal gyrus, and frontal pole of ECTS patients (P < 0.05). Rs-fMRI analysis showed absolute values of 20 FCs were significantly decreased in ECTS patients compared with MRI controls, which connected 16 distinct ROIs. The average SUVR of right caudate and the average of 20 FCs were positively correlated with PSI in ECTS patients (P = 0.034 and P = 0.005, respectively).

Conclusion

This study indicated that ECTS patients presented significantly reduced PSI, which is closely associated with decreased SUVR and FC of cortical–striatal circuit. Caudate played an important role in processing speed dysfunction.

Clinical trial registration

NCT04954729; registered on July 8, 2021, public site, https://clinicaltrials.gov/ct2/show/NCT04954729

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05740-w.

Aerobic exercise in older people with subclinical sporadic cerebral small vessel disease: A randomized clinical trial

INTRODUCTION

The benefit and risk of aerobic exercise among older people harboring advanced cerebral small vessel disease (CSVD) upon cognition, mood, and motor functions are unknown.

METHODS

This rater-blind randomized trial examined effects of a 24-week aerobic exercise training (60 min/session, twice/week) upon clinical (cognition, mood, motor functions) and hemodynamic (pulse pressure [PP], blood pressure [BP], pulsatility index) measures in older people harboring moderate to severe CSVD, as evidenced by confluent white matter hyperintensity and/or ≥2 lacunes on magnetic resonance imaging. We further investigated interactions between treatment conditions and hemodynamics measures.

RESULTS

Fifty-three and 54 subjects were randomized into the active and control group, respectively. There was no between-group difference in any of the clinical outcomes. The active group had a greater between-group reduction in systolic BP and PP than the control group. Within-group comparison showed that global cognition of the active group remained similar at end of the study compared to baseline, whereas it declined significantly in the control group. We observed "diverging" interaction effects in that greater reduction in systolic BP/PP was associated with greater improvement in memory functions and global cognition but worsening in processing speed in the active group. Side effects were comparable between the two groups.

DISCUSSION

Future study should investigate the mechanisms of the diverging impacts of aerobic exercise upon different cognitive domains so that the benefit-risk ratio of aerobic exercise in older people harboring more advanced CSVD can be better defined.

A multiparametric MRI study of structural brain damage in dementia with lewy bodies: A comparison with Alzheimer's disease

INTRODUCTION

Differential diagnosis between dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) is crucial for an adequate patients' management but might be challenging. We investigated with advanced MRI techniques gray (GM) and white matter (WM) damage in DLB patients compared to those with AD.

METHODS

24 DLB patients, 26 age- and disease severity-matched AD patients, and 20 age and sex-matched controls performed clinical and neuropsychological assessment, and brain structural and diffusion-tensor MRI. We measured GM atrophy using voxel-based morphometry, WM hyperintensities (WMH) using a local thresholding segmentation technique, and normal-appearing WM (NAWM) damage using tract-based spatial statistic.

RESULTS

DLB and AD patients exhibited mild-to-moderate-stage dementia. Compared to controls, GM damage was diffuse in AD, while limited to bilateral thalamus and temporal regions in DLB. Compared to DLB, AD patients exhibited GM atrophy in bilateral fronto-temporal and occipital regions. DLB and AD patients showed higher WMH load than controls, with no differences among each other. WMH in DLB were diffuse with relative prevalence in posterior parietal-occipital regions. Compared to controls, both DLB and AD patients showed reduced microstructural integrity of the main supratentorial and infratentorial NAWM tracts. AD patients exhibited greater posterior NAWM damage than DLB.

CONCLUSIONS

DLB showed prominent WM degeneration compared to the limited GM atrophy, while in AD both tissue compartments were severely involved. In DLB, NAWM microstructural degeneration was independent of WMH, thus revealing two possible underlying processes. Different pathophysiological mechanisms are likely to drive GM and WM damage distribution in DLB and AD.

Preservation Analysis on Spatiotemporal Specific Co-expression Networks Suggests the Immunopathogenesis of Alzheimer's Disease

The occurrence and development of Alzheimer’s disease (AD) is a continuous clinical and pathophysiological process, molecular biological, and brain functional change often appear before clinical symptoms, but the detailed underlying mechanism is still unclear. The expression profiling of postmortem brain tissue from AD patients and controls provides evidence about AD etiopathogenesis. In the current study, we used published AD expression profiling data to construct spatiotemporal specific coexpression networks in AD and analyzed the network preservation features of each brain region in different disease stages to identify the most dramatically changed coexpression modules and obtained AD-related biological pathways, brain regions and circuits, cell types and key genes based on these modules. As result, we constructed 57 spatiotemporal specific networks (19 brain regions by three disease stages) in AD and observed universal expression changes in all 19 brain regions. The eight most dramatically changed coexpression modules were identified in seven brain regions. Genes in these modules are mostly involved in immune response-related pathways and non-neuron cells, and this supports the immune pathology of AD and suggests the role of blood brain barrier (BBB) injuries. Differentially expressed genes (DEGs) meta-analysis and protein–protein interaction (PPI) network analysis suggested potential key genes involved in AD development that might be therapeutic targets. In conclusion, our systematical network analysis on published AD expression profiling data suggests the immunopathogenesis of AD and identifies key brain regions and genes.

Cerebrovascular disease, neurodegeneration, and clinical phenotype in dementia with Lewy bodies

We investigated whether cerebrovascular disease contributes to neurodegeneration and clinical phenotype in dementia with Lewy bodies (DLB). Regional cortical thickness and subcortical gray matter volumes were estimated from structural magnetic resonance imaging (MRI) in 165 DLB patients. Cortical and subcortical infarcts were recorded and white matter hyperintensities (WMHs) were assessed. Subcortical only infarcts were more frequent (13.3%) than cortical only infarcts (3.1%) or both subcortical and cortical infarcts (2.4%). Infarcts, irrespective of type, were associated with WMHs. A higher WMH volume was associated with thinner orbitofrontal, retrosplenial, and posterior cingulate cortices, smaller thalamus and pallidum, and larger caudate volume. A higher WMH volume was associated with the presence of visual hallucinations and lower global cognitive performance, and tended to be associated with the absence of probable rapid eye movement sleep behavior disorder. Presence of infarcts was associated with the absence of parkinsonism. We conclude that cerebrovascular disease is associated with gray matter neurodegeneration in patients with probable DLB, which may have implications for the multifactorial treatment of probable DLB.

Identifying Imaging Genetics Biomarkers of Alzheimer's Disease by Multi-Task Sparse Canonical Correlation Analysis and Regression

Imaging genetics combines neuroimaging and genetics to assess the relationships between genetic variants and changes in brain structure and metabolism. Sparse canonical correlation analysis (SCCA) models are well-known tools for identifying meaningful biomarkers in imaging genetics. However, most SCCA models incorporate only diagnostic status information, which poses challenges for finding disease-specific biomarkers. In this study, we proposed a multi-task sparse canonical correlation analysis and regression (MT-SCCAR) model to reveal disease-specific associations between single nucleotide polymorphisms and quantitative traits derived from multi-modal neuroimaging data in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. MT-SCCAR uses complementary information carried by multiple-perspective cognitive scores and encourages group sparsity on genetic variants. In contrast with two other multi-modal SCCA models, MT-SCCAR embedded more accurate neuropsychological assessment information through linear regression and enhanced the correlation coefficients, leading to increased identification of high-risk brain regions. Furthermore, MT-SCCAR identified primary genetic risk factors for Alzheimer’s disease (AD), including rs429358, and found some association patterns between genetic variants and brain regions. Thus, MT-SCCAR contributes to deciphering genetic risk factors of brain structural and metabolic changes by identifying potential risk biomarkers.

Update on neuroimaging in non-Alzheimer's disease dementia: a focus on the Lewy body disease spectrum

PURPOSE OF REVIEW

An accurate differential diagnosis between Alzheimer's disease (AD) and non-AD dementia is of paramount importance to study disease mechanisms, define prognosis, and select patients for disease-specific treatments. The purpose of the present review is to describe the most recent neuroimaging studies in Lewy body disease spectrum (LBDS), focusing on differences with AD.

RECENT FINDINGS

Different neuroimaging methods are used to investigate patterns of alterations, which can be helpful to distinguish LBDS from AD. Positron emission tomography radiotracers and advanced MRI structural and functional methods discriminate these two conditions with increasing accuracy. Prodromal disease stages can be identified, allowing an increasingly earlier diagnosis.

SUMMARY

Neuroimaging biomarkers can aid in obtaining the best diagnostic accuracy in LBDS. Despite the main role of neuroimaging in clinical setting is to exclude secondary causes of dementia, structural and metabolic imaging techniques give an essential help to study in-vivo pathophysiological mechanisms of diseases. The importance of neuroimaging in LBDS is given by the increasing number of imaging biomarker developed and studied in the last years.

Greater Social Engagement and Greater Gray Matter Microstructural Integrity in Brain Regions Relevant to Dementia

OBJECTIVE

Social engagement (SE) may protect against cognitive decline in older adults. We estimate associations of SE with gray matter (GM) microstructure in regions of interest (ROI) relevant to social cognition, among community-dwelling older adults.

METHOD

Cross-sectional analysis of 293 Health ABC study participants who underwent 3 Tesla magnetic resonance imaging with diffusion tensor and free from cognitive impairment was conducted. Linear regression models tested associations between SE index (marital status, not living alone, social activities, work, and volunteering) and mean diffusivity (MD) of GM ROIs, adjusted for age, race, gender, and education. Hearing and activities of daily living (ADL) difficulties were tested as confounders. Effect modification by gender was tested with interaction terms and stratification by gender.

RESULTS

Higher SE was significantly related to lower MD (greater GM microstructural integrity) (shown as standardized estimate [p-value]) in left middle frontal gyrus-orbital part: -.168 (.005), left caudate nucleus: -.141 (.02), left temporal pole-middle temporal gyrus: -.136 (.03), right middle frontal gyrus: -.160 (.006), right superior frontal gyrus-orbital part: -.187 (.002), and right middle frontal gyrus-orbital part: -.124 (.04), when adjusted for demographic attributes. Associations were robust to adjustments for hearing or ADL difficulty. There was significant effect modification by gender for some ROIs, with associations only for females.

DISCUSSION

SE is related to greater microstructural integrity of specific GM regions relevant to social cognition, that have described roles in dementia. SE may therefore be a useful preventive mechanism against loss of GM integrity in older adults.